Communication unit and method of channel estimation in an ofdm communication system

ABSTRACT

A method of channel estimation in a wireless orthogonal frequency division multiplexed (OFDM) communication system is disclosed. The method comprises receiving a signal in the time domain and applying a Fourier transform to the received signal to obtain a frequency domain signal including a plurality of sub-carriers. Then, the method requires estimating probabilities of coded bits for the plurality of frequency domain sub-carriers and performing channel coefficient estimation for the plurality of frequency domain sub-carriers using channel coefficient estimates for another of the plurality of frequency domain sub-carriers.

FIELD OF THE INVENTION

This invention relates to an estimation of radio-channel propagationconditions, in order to equalise their effect on transmitted data. Theinvention is applicable to improving an accuracy of channel estimationin an orthogonal frequency division multiplex (OFDM) wireless receiver.

BACKGROUND OF THE INVENTION

Wireless communication systems, for example cellular telephony orprivate mobile radio communication systems, typically provide for radiotelecommunication links to be arranged between a plurality of basetransceiver stations (BTSs) and a plurality of subscriber units, oftentermed mobile stations (MSs).

Wireless communication systems are distinguished over fixedcommunication systems, such as the public switched telephone network(PSTN), principally in that mobile stations move between BTS (and/ordifferent service providers) and in doing so encounter varying radiopropagation environments.

Methods of communicating information simultaneously exist wherecommunication resources in a communication network are shared by anumber of users. Such methods are termed multiple access techniques. Anumber of multiple access techniques exist, such as frequency divisionmultiple access (FDMA), time division multiple access (TDMA), and codedivision multiple access (CDMA).

The communication link from a BTS to a MS is referred to as thedown-link. Conversely, the communication link from a MS to the BTS isreferred to as the up-link.

In wireless communication systems, there is a need to estimate theeffect caused by the ‘wireless communication channel’ on the data beingtransmitted. Channel estimation is required so that the received datacan be equalised to reduce, restore or minimise signal degradationcaused by such transmission channel impairments. A radio channel is saidto be equalised if the channel impairments can be eliminated orsignificantly reduced.

In the field of this invention, the most conventional strategy inestimating radio channel propagation conditions is to model the radiochannel by a finite impulse response (FIR) filter. The channelestimation is typically performed by periodically transmitting a knowndata sequence, generally referred to as a training sequence, over thedesired radio channel. Such training sequences are a-priori known by thereceiver. The training sequence is extracted from the received, desireddata stream and is used to compute channel estimates.

In the field of this invention, namely that of Orthogonal FrequencyDivision Multiplexed (OFDM) systems, also termed multi-carrier systems,the data stream is divided into several (N) sub-streams. Thesesub-streams are transmitted on ‘N’ orthogonal sub-carriers at differentfrequencies, for example by means of an inverse fast Fourier transform(IFFT) at the transmitting unit.

One of the main advantages of OFDM systems is their very simpleequalization scheme, which is reduced to a multiplication of the FFToutputs (of each carrier frequency) by the frequency domain channelcoefficients. Of course, the true channel coefficients are unknown, andhave to be estimated. As an illustration, the mean square error betweenthe true channel coefficients and the channel coefficients at time 0(time 0 being chosen when a training symbol is received) is plotted infunction of time on FIG. 1, for an HIPERLAN/2 channel with a mobileterminal moving at 3 m/s.

Referring now to FIG. 2, a classical Bit interleaved and convolutionallycoded (BICC) OFDM system 200 is shown. The BICC OFDM system 200 includesa BICC OFDM transmitter 210. The BICC transmitter 210 receives a datastream 212 d_(i) and convolutionally codes 215 the data stream toproduce an output b_(i) 217. The convolutionally coded output b_(i) 217is input to a bit interleaver 220. The output from the bit interleaver220 is then input to a mapping function 225, which associates a subsetof bits x_(k) 228 to a location on a constellation. The type ofconstellation generated by the transmitter 210 is dependent upon themodulation scheme employed by the OFDM modulator 230. The OFDM modulatorthen outputs the OFDM modulated signal over the communication channel235.

A BICC OFDM receiver 250 receives the OFDM modulated signal over thecommunication channel 235. In effect, the receiver 250 performs theinverse operations of the BICC OFDM transmitter 210. In this regard, thereceiver 250 includes an OFDM demodulator 255 to translate the receivedconstellation locations into a sequence of bit subsets. The bit subsetsare then input to a demapping function 260. In the demapping function,bit metrics are computed to feed the Viterbi decoder 270. Thiscomputation involves the frequency channel coefficients, which must beestimated periodically. A bit de-interleaving function 265 arranges thereceived bit-stream for decoding in the Viterbi decoder 270.

The above receiver arrangement presents a number of significantdrawbacks. In particular, the process to de-map, decode and performchannel estimation separately, as three distinct and independentfunctions, only enables the respective individual operations to beoptimised.

In J. Boutros, C. Lamy, F. Boixadera, “Bit-interleaved coded modulationfor multiple-input multiple-output channels”, IEEE ISSSTA, September2000, a method performing joint de-mapping, decoding and channelestimation using an estimation-maximisation (EM) algorithm and aturbo-demodulation procedure has been proposed. However, the proposedalgorithm has been derived in a single carrier context and is thereforeunsuitable for OFDM implementation.

A turbo channel estimation method using EM algorithm in an OFDM contexthas been proposed in E. Jaffrot, M. Siala, “Turbo channel estimation forOFDM systems on highly time and frequency selective channels”, Proc.ICASSP2000. However, the channel estimation method assumes, as apre-requisite, a phase-shift keyed (PSK) modulation scheme. As such, themethod is limited in its applications and works only for binary PSK(BPSK) and quadrature PSK (QPSK) mapping. Thus, it cannot be applied tohigh bit rate applications such as HIPERLAN/2 (which uses a quadratureamplitude modulation (QAM) scheme). Moreover, it uses pilot carriers,which reduces the application bit rate.

Thus, there exists a need in the field of the present invention toprovide an improved channel estimation method in an OFDM communicationsystem, wherein at least some of the aforementioned disadvantages may bealleviated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a graph of mean square error (MSE) for a BRAN Achannel with a mobile terminal (MT) moving at 3 m/s; and

FIG. 2 shows a simplified processing block diagram of a bit-interleavedand convolutionally coded OFDM communication system.

Exemplary embodiments of the present invention will now be described,with reference to the accompanying drawings, in which:

FIG. 3 illustrates a wireless communication unit adapted in accordancewith a preferred embodiment of the present invention;

FIG. 4 illustrates a channel estimation model applicable to thepreferred embodiment of the present invention.

FIG. 5 illustrates a block diagram of a decoding unit adapted inaccordance with a preferred embodiment of the present invention;

FIG. 6 illustrates two examples of mapping that can be used in thepreferred embodiment of the present invention;

FIG. 7 illustrates a model of an OFDM system used in the preferredembodiment of the present invention;

FIG. 8 illustrates a block diagram of a decoding unit's forward-backwardalgorithm in accordance with a preferred embodiment of the presentinvention; and

FIG. 9 shows bit-error-rate simulation results of the channel estimatoraccording to the preferred embodiment of the invention.

DESCRIPTION OF PREFERRED EMBODIMENTS

In summary, the preferred embodiments of the invention described belowperform blind channel estimation for each OFDM symbol, which allows thereceiver to track the channel variations, and consequently to increasethe time between training sequences. In particular, the channel estimateof the frequency channel coefficients for each OFDM symbol is performedusing an Expectation-Maximization (EM) algorithm that includes estimatedchannel coefficients using at least one other, and preferably all theother sub-carriers.

Referring now to FIG. 3, there is shown, by way of example, a blockdiagram of a wireless communication unit, adapted to support theinventive concepts of the preferred embodiments of the presentinvention. The wireless communication unit 300 contains an antenna 302preferably coupled to an antenna switch 304 that provides isolationbetween a receiver and a transmitter chain within the wirelesscommunication unit 300.

The receiver chain shown includes receiver front-end circuitry 306(effectively providing reception, filtering and intermediate orbase-band frequency conversion). The front-end circuit 306 is seriallycoupled to a signal processing function 308. An output from the signalprocessing function is provided to a suitable output device 310.

The signal processing function 308 performs all signal processingfunctions for the wireless communication unit 300, includingdemodulation, de-mapping, bit de-interleaving, channel estimation anddecoding. In accordance with the preferred embodiments of the presentinvention, the signal processing unit 308 has been adapted to performEM-based channel estimation, using probabilities on coded bits. Theseprobabilities may be provided by any channel decoding scheme yieldingsoft outputs, e.g. a so-called BCJR algorithm—see L. Bahl, J. Cocke, F.Jelinek and J. Raviv, “Optimal Decoding of Linear Codes for MinimizingSymbol Error Rate”, IEEE transaction on Information Theory, March 1974.The preferred embodiment uses a turbo-demodulation process, such that itis able to jointly perform de-mapping and decoding of the receivedsignal. The signal processing function 308 is further adapted inasmuchas it employs a new channel estimation method, as is further describedwith regard to FIG. 5.

The receiver chain shown also includes received signal strengthindicator (RSSI) circuitry 312 (shown coupled to the receiver front-end306, although the RSSI circuitry 312 could be located elsewhere withinthe receiver chain). The RSSI circuitry is coupled to a controller 314for maintaining overall subscriber unit control. The controller 314 isalso coupled to the receiver front-end circuitry 306 and the signalprocessing function 308 (generally realised by a digital signalprocessor (DSP)). The controller 314 may therefore receive bit errorrate (BER) or frame error rate (FER) data from recovered information.The controller 314 is coupled to the memory device 316 for storingoperating regimes, such as decoding/encoding functions and the like. Atimer 318 is typically coupled to the controller 314 to control thetiming of operations (transmission or reception of time-dependentsignals) within the wireless communication unit 300.

As regards the transmit chain, this essentially includes an input devicecoupled in series via transmit signal processor 328 to atransmitter/modulation circuit 322. Thereafter, any transmit signal ispassed through a power amplifier 324 to be radiated from the antenna302. The transmitter/modulation circuitry 322 and the power amplifier324 are operationally responsive to the controller, with an output fromthe power amplifier coupled to the duplex filter or circulator 304. Thetransmitter/modulation circuitry 322 and receiver front-end circuitry306 comprise frequency up-conversion and frequency down-conversionfunctions (not shown).

Of course, the various components within the wireless communication unit300 can be arranged in any suitable functional topology that is able toutilise the inventive concepts of the present invention. Furthermore,the various components within the wireless communication unit 300 can berealised in discrete or integrated component form, with an ultimatestructure therefore being merely an arbitrary selection. The preferreddecoding and channel estimation function can be implemented in software,firmware or hardware, with the function being implemented in a softwareprocessor (or indeed a digital signal processor (DSP)) that performs thedecoding function, merely a preferred option.

The transmitter signal processor 328, which is shown as being distinctfrom the receiver signal processor 308 for clarity purposes only,includes a convolutional encoder or a turbo-coding function.

In accordance with a preferred embodiment of the present invention, animproved channel estimation algorithm for the OFDM system is included.The channel estimation uses an Expectation-Maximisation (EM) algorithm.This is a parametric estimation algorithm, which attempts to estimatethe parameters ‘θ’ 404 of a system 400 from the observations of itsoutput y 406 blindly without knowing its input x 402, as shown in themodel in FIG. 4.

The EM algorithm is an iterative procedure, which maximizes thelikelihood function in two steps:

A first Expectation step (E-step): which comprises a computation of:E_(x)[P(x|y,θ^((p)))]  [1]andA second Maximization step (M-step): $\begin{matrix}{\theta^{({p + 1})} = {\underset{\theta}{argmax}\quad{P\left( {\left. y \middle| \theta \right.,{\hat{x}}^{(p)}} \right)}}} & \lbrack 2\rbrack\end{matrix}$In a practical implementation, the maximisation (M-)step is performed bymaximizing the auxiliary function:Q(θ,θ^((p)))=E _(x)[log P(x,y|θ)|y,θ ^((p))]  [3]

In accordance with the preferred embodiment of the present invention,the auxiliary function of the channel estimation algorithm has beenadapted to take into account OFDM specificities. Indeed, the auxiliaryfunction has been rewritten as:Q(H _(m) ,H _(m) ^((p)))=E _(x) _(x) [log P(x _(m) ,y _(m) ,{tilde over(H)} ^((m)) |H _(m))|y _(m) ,H _(m) ^((p))]  [4]where: {tilde over (H)}^((m)) stands for the vector H with a 0 on them^(th) component. This allows, when estimating the channel coefficientof the m-th sub-carrier, to use and take advantage of the priorestimations of the channel coefficients of the other frequency domainsub-carriers.

In addition, the Expectation step has been configured to be anestimation of the probability on decoded bits, which are needed for theM-step. These probabilities are provided by any decoding scheme yieldingsoft outputs. Furthermore, the preferred embodiment of the presentinvention, proposes to use a turbo-demodulation procedure, in order tooptimise jointly the de-mapping and the decoding steps. In this manner,the decoding algorithm leads to a much better performance than anon-iterative decoding scheme.

However, it is within the contemplation of the invention that theinventive concepts herein described are applicable to any convolutionalencoder, i.e. it is applicable for any rate and any puncturing scheme.Furthermore, the decoding scheme of the present invention can be appliedto any encoded data that can be decoded by a soft output decoder, i.e.any convolutional or turbo decoder.

Referring now to FIG. 5, a block diagram model 500 of the preferredchannel estimation procedure is illustrated. The received data stream505 is demodulated in OFDM demodulation function 510. The output fromthe demodulation function 510 is input to a channel estimation function,which incorporates an estimation function 520 and a maximizationfunction 550.

The OFDM demodulated signal is input to a de-mapping function 525, whichoutputs de-mapped signals to a de-interleaving function 530, andthereafter to a soft output decoder function 535. In this manner, thechannel estimation function is operational over both the de-mapping anddecoding functions.

The de-mapping function in the preferred embodiment, i.e. the functionthat associates a subset of bits to a modulation constellation location,is able to operate with any labelling map. For example, as shown in FIG.6, the de-mapping operation may be performed on a subset of a 16-QAMsignal, which is composed of four bits. Each subset has to be associatedto one of the sixteen positions of the constellation. There are a numberof different ways to define the association function, which affect theperformance of the decoding system. For example, constellation 610 inFIG. 6 illustrates a Gray labelling structure, which is deemed optimumwhen a Viterbi decoder is used. However, when turbo-demodulation isused, an alternative mapping format such as the set partitioning format620 may provide better results.

The output from the decoding function 535, is fed back to the de-mappingfunction 525 via an interleaving function 540. The output from theimproved estimation function 520 is input to a maximization function550. The output from the maximization function 550 is input to thede-mapping function 525.

It is noteworthy that this arrangement also yields decoded bits, in abit-stream 535. The bit decisions are made from decision function 560.Thus, the improved channel estimation arrangement is a joint channelestimation and decoding scheme.

In accordance with the preferred embodiment of the present invention,the channel estimation operation has been adapted to take into accountthe OFDM specificities, using the coded bits on neighbouring frequencychannels

Referring now to FIG. 7, a block diagram of a parallel equivalentrepresentation of a cyclic-prefix OFDM modulator-demodulator 700 isillustrated. In effect, an OFDM transmitter wishes to emit a set of bits$\left\{ b_{n}^{(m)} \right\}_{\underset{1 \leq n \leq N}{1 \leq m \leq M}}702$over a channel {h_(l)}_(l≦l≦L) using, say, a 2^(N)-QAM scheme 704. Firstevery subset of N bits {b_(n) ^((m))}_(1≦n≦N) 702 is modulated into asymbol x_(m). In the OFDM context, the {x_(m)}_(l≦m≦M) symbols 706 aretransformed from a frequency domain representation into a time domainusing an inverse fast fourier transform (IFFT). The time-domain signalsare then serialized and convolved by the channel filter. The signals areeffectively corrupted by noise {n_(m)}_(1≦m≦M) 732 before they arefinally returned into the frequency domain using a FFT (not shown) toproduce an output signal {y_(m)}_(l≦m≦M) 734.

It is noteworthy that in a standard OFDM transmitted signal, the signalis confined into a spectrum mask by precluding some carriers from beingused, especially the ones at the top and the bottom of the FFT. In thisregard, such carrier values are set to zero.

An important characteristic of OFDM communication systems, utilised inthe preferred embodiment of the present invention, is that OFDM systemsare dimensioned so that the length of the channel impulse response(‘CIR’) is smaller than the cyclic prefix size. Accordingly, it ispossible to use the classical OFDM system model representation of FIG.7.

Let us consider the case where h is a vector of M independent variables,with its first L components being L independent Rayleigh fading valueswith variance σ₁ ², 1=1, . . . , L. Let us further assume that the M-Lother components are independent Rayleigh fadings with a small varianceε².

If the variance ε is small enough, h has been determined as providing areasonably good model of the time domain channel. This leaves thefrequency domain channel as being represented by: $\begin{matrix}{{H = {Fh}},{{{with}\quad F} = {\frac{1}{\sqrt{M}}\left\lbrack {\mathbb{e}}^{{- 2}{\mathbb{i}\pi}\frac{lk}{M}} \right\rbrack}_{{({l,k})} \in {\lbrack{1,M}\rbrack}^{2}}}} & \lbrack 5\rbrack\end{matrix}$

In accordance with the preferred embodiment of the present invention,let us define a new auxiliary function that provides an improvedestimate of a channel coefficient H_(m). It is noteworthy that theimproved channel estimate considers the channel coefficients on allother carriers in order to refine/track the primary channel coefficient.

The inventors have appreciated that knowledge of the channelcoefficients from other carriers is useful, as the decoder needs anestimate of the frequency channel coefficients {H_(m)}_(l≦m≦M) toeradicate the channel effects from the received coded bits. Indeed, inthe preferred embodiment of the present invention, the frequency channelcoefficients from the Fourier transform of the time channel coefficients{h_(l)}_(l≦l≦L) are utilised in this regard.

Thus:H=Fh   [6]Where:

H is a column vector of {H_(m)}_(l≦m≦M),

-   -   h is a column vector of {h_(l)}_(l≦l≦L) filled with zeros for        the M-L last coefficients, and

F is a ‘M×M’ matrix filled with Fourier coefficients.

At the opposite end of the time domain, the values of elements of H, areunknown. Indeed, due to the introduction of white noise in the model, itcan be readily assumed that all of the elements are corrupted.

The inventors of the present invention have appreciated that a standardchannel estimation method, such as${{\overset{\Cap}{H}}_{m} = \frac{y_{m}}{x_{m}}},$where x_(m) is a known symbol, leads to a poor result.

In contrast, the proposed method of using the knowledge of the channelcoefficients from other carriers helps to refine the values of channelestimate. In particular, the definition of the statistical model h andthe cost function are key distinguishing features of the inventiveconcepts of the present invention.

Thus, in the auxiliary function, we add {tilde over (H)}^((m)) in thefollowing manner:Q(H _(m) ,H _(m) ^((p)))=E _(x) _(m) [log P(x _(m) ,y _(m) ,{tilde over(H)} ^((m)) H _(m))|y _(m) ,H _(m) ^((p))]  [7]Where:

{tilde over (H)}^((m)) stands for the vector H with a 0 on the m^(th)component.

The maximization of this function leads, after some computationaleffort, to the following channel estimate update formula:$\begin{matrix}{{H_{m}^{({p + 1})} = \frac{\sum\limits_{x_{m}}{{P\left( {{y_{m}❘x_{m}},H_{m}^{(p)}} \right)}{{P\left( x_{m} \right)}\left\lbrack {{y_{m}\overset{\_}{x_{m}}} - {{\sigma^{2}\left( \Delta_{SH}^{- 1} \right)}_{m}{\overset{\sim}{H}}^{(m)}}} \right\rbrack}}}{\sum\limits_{x_{m}}{{P\left( {{y_{m}❘x_{m}},H_{m}^{(p)}} \right)}{{P\left( x_{m} \right)}\left\lbrack {{x_{m}}^{2} - \frac{\sigma^{2}}{v^{2}} + \frac{v^{2}}{\gamma^{2}}} \right\rbrack}}}}{{where}:}} & \lbrack 8\rbrack \\{{{\gamma^{2} = \left\lbrack \Delta_{SH}^{- 1} \right\rbrack_{({m,m})}},{\Delta_{SH} = {{SF}^{*}{{Diag}\left( \left\lbrack {\sigma_{1}^{2}\quad\ldots\quad\sigma_{L}^{2}ɛ^{2}\quad\ldots\quad ɛ^{2}} \right\rbrack \right)}{FS}^{T}}}}{and}} & \lbrack 9\rbrack \\{v^{2} = {{\frac{1}{M}\left\lbrack {{\sum\limits_{l = 1}^{L}\sigma_{l}^{2}} + {\left( {M - L} \right)ɛ^{2}}} \right\rbrack}.}} & \lbrack 10\rbrack\end{matrix}$

S is the information carriers selection matrix. When dealing with a fullsub-carrier OFDM symbol, γ⁻² can be rewritten into: $\begin{matrix}{\gamma^{- 2} = {{\frac{1}{M}{\sum\limits_{l = 1}^{L}\sigma_{l}^{2}}} + {\frac{M - L}{M}{ɛ^{2}.}}}} & \lbrack 11\rbrack\end{matrix}$

It is noteworthy that the general expression of γ⁻² of equation [9]takes into account the fact that some sub-carriers are not used, whichis always the case in practice.

In practice, three parameters are needed to implement the aforementionedformula, namely: P(y_(m)|x_(m),H_(m) ^((p))), P(x_(m)) and {tilde over(H)}^((m)).

If we assume that the channel additive noise is a Gaussian white noiseof variance σ², P(y_(m)|x_(m),H_(m) ^((p))) may be viewed as:$\begin{matrix}{{P\left( {{y_{m}❘x_{m}},H_{m}^{(p)}} \right)} = {\frac{1}{{\pi\sigma}^{2}}{\mathbb{e}}^{\frac{{{y_{m} - {H_{m}^{(p)}x_{m}}}}^{2}}{\sigma^{2}}}}} & \lbrack 12\rbrack\end{matrix}$Where:

P(x_(m)) is the product of the probabilities of each coded bit involvedin the symbol x_(m). The turbo-demodulation process provides anestimation of these probabilities. Thus, P(x_(m)) is the result of theE-step; and

{tilde over (H)}^((m)) is unknown, but each of its component {tilde over(H)}_(l) ^((m)) may be taken as the current estimate of the channelcoefficient, i.e. {tilde over (H)}_(l) ^((m))=H_(l) ^(p+1)) for i<m and{tilde over (H)}_(l) ^((m))=H_(l) ^((p)) for i>m.

However, the inventors of the present invention have recognised that theabove methodology would lead to the last set of coefficients beingestimated more accurately than the first set of coefficients. Hence, toremove this drawback and improve the estimation, the inventors proposeimplementing a ‘forward-backward’ procedure to further improve thechannel refinement.

The preferred implementation of the forward/backward process isillustrated in FIG. 8. The channel refinement is performed on acarrier-by-carrier basis. Received data is input to the Turbodemodulation estimation process 804, as illustrated in FIG. 6.Furthermore, the received data is also input to the maximization process806, which comprises an algorithm having two steps, i.e. one ‘forward’step 808 and one ‘backward’ step 810. The operation of the forward step808 and the backward step 810 is performed on the received data and thechannel estimation values output 812 from the estimation algorithm 804.The operation of the forward step 808 and the backward step 810 isperformed making bit decisions 814 using the information bitprobabilities. The output of the process is a sequence of received bits816.

In accordance with the preferred embodiment of the present invention,the forward step is implemented as follows:

The H_(m) coefficients are estimated from m=1 to m=M.

In this step, when computing the channel coefficient on sub-carrier m,the channel coefficient estimates on the other sub-carriers l, such thatm<l≦M, come from the previous iteration. The channel coefficientestimates on the other sub-carriers l such that l(‘one’)≦l<m come fromthe actual iteration.

The backward step is implemented as follows:

The H_(m) coefficients are estimated from m=M to m=1, using theestimates of the forward step.

In this step, when computing the channel coefficient on sub-carrier m,the channel coefficient estimates on the other sub-carriers l such thatm<l≦M come from the forward step, and the channel coefficient estimateson the other sub-carriers l such that 1(‘one’)≦l<m come from the presentbackward step.

In this manner, approximately the same level of accuracy of each channelcoefficient can be achieved, thereby improving the channel estimateperformance.

To fully evaluate the performance of this method, a set of Monte-Carlosimulations was performed over two hundred channels to estimate the biterror rate (BER) and the packet error rate (PER). The simulation contextwas performed using one hundred OFDM symbols of sixty-four sub-carrierspreceded by two pilot symbols. This is useful in obtaining a firstchannel estimate. The simulation was performed over sixteen independentRayleigh fading channels. A half rate convolutional encoder (035, 023)was used. Symbols were bit-interleaved with a random pattern andmodulated by a 16-QAM constellation.

The results of the simulation are illustrated in FIG. 9 and FIG. 10.Referring now to FIG. 9, a comparison of the average BER versus signalto noise ratio (SNR) is illustrated for an OFDM system. FIG. 10illustrates a comparison of the average PER versus SNR for the OFDMsystem.

In both FIG. 9 and FIG. 10, the OFDM system does not refine channelestimates and performs only two iterations of the turbo-demodulator perglobal iteration. Three curves are shown:

(i) A first curve indicates the performance without employing theinventive concepts herein before described, identified as ‘NONE 2 TD’;

(ii) A second curve illustrates the performance when implementing theproposed invention, identified as ‘1 EM-OFDM-SS 2 TD for ε²=3e⁻³; and

(iii) A third curve illustrates the performance when implementing theproposed invention, identified as ‘1 EM-OFDM-SS2 2 TD’ for ε²=1e⁻¹⁶.

It is noteworthy that, by implementing the inventive concepts hereindescribed, an improvement of around 2 dB in BER/PER can be achieved.Moreover, provided that the variance ε parameter is small enough thesetting of the parameter is not critical. Hence, the user does not haveto perform precise tuning on the variance ε parameter.

It is within the contemplation of the invention that the aforementionedinventive concepts can be applied to any element in the communicationsystem that performs decoding and channel estimation, for example a BTScommunication unit and/or a MS.

In the preferred embodiment of the present invention, the maximizationstep utilises a forward-backward approach. However, it is within thecontemplation of the invention that alternative maximization techniquesmay be used in the decoder such that the decoder is still able tobenefit from the inventive concepts described herein.

It will be understood that a method of channel estimation performed by acommunication unit operating in an OFDM communication system asdescribed above, tends to provide at least some of the followingadvantages:

(i) A mechanism to perform jointly de-mapping, decoding and channelestimation is described, whereby their performance can be globallyoptimised. In particular, the modification of the EM cost functionresults in the modification of the channel update formula, whichprovides an improved decoding performance.

(ii) There is no estimation error in a noiseless case. In practice, thismeans that the proposed estimator performs better than thestrongest-path method based estimator for high SNR conditions.

(iii) Estimation noise is partially removed from the selected channelcoefficients.

(iv) There is minimal additional complexity when compared to prior artarrangements.

(v) The inclusion of OFDM specificities in a novel and inventiveapproach enables bit-interleaved coded modulation improvements proposedby J. Boutros et al. to be translated to an OFDM scenario. Inparticular, the inventive concepts can be applied to any kind ofmapping, and does not use pilot carriers. Nevertheless, the inventiveconcepts hereinbefore described may be used in the presence of pilotcarriers, if desired.

Whilst specific, and preferred, implementations of the present inventionare described above, it is clear that one skilled in the art couldreadily apply further variations and modifications of such inventiveconcepts.

Thus a method of channel estimation performed by a communication unitoperating in an OFDM communication system has been provided wherein atleast some of the aforementioned disadvantages with prior artarrangements have been alleviated.

1. A method of channel estimation in a wireless orthogonal frequencydivision multiplexed (OFDM) communication system, comprising the stepsof: receiving a signal in time domain; applying a Fourier transform tosaid received signal to obtain a frequency domain signal including aplurality of sub-carriers; estimating probabilities of coded bits for atleast said plurality of frequency domain sub-carriers; and performingchannel coefficient estimation for at least said plurality of frequencydomain sub-carriers using channel coefficient estimates for at least oneother of said plurality of frequency domain sub-carriers.
 2. A method ofchannel estimation according to claim 1, wherein said step of performingchannel coefficient estimation for substantially each of said pluralityof frequency domain sub-carriers uses channel coefficient estimationbenefits from said channel coefficient estimates for substantially allthe other frequency domain sub-carriers of said plurality.
 3. A methodof channel estimation according to claim 2, wherein said plurality offrequency domain sub-carriers comprises substantially all thesub-carriers of said frequency domain signal.
 4. A method of channelestimation according to claim 1 further comprising repeating said stepsof estimating probabilities and performing channel coefficientestimation so as to improve iteratively an accuracy of said channelcoefficient estimates.
 5. A method of channel estimation according toclaim 4, wherein a kth channel coefficient estimation is substantiallyin accordance with the following equation:$H_{k}^{({p + 1})} = \frac{{P\left( {{y_{k}❘x_{k}},H_{k}^{(p)}} \right)}\left\lbrack {{y_{k}\overset{\_}{x_{k}}} - {{\sigma^{2}\left( \Delta^{- 1} \right)}_{k}{\overset{\sim}{H}}^{(k)}}} \right\rbrack}{{P\left( {{y_{k}❘x_{k}},H_{k}^{(p)}} \right)}\left\lbrack {{x_{k}}^{2} - \frac{\sigma^{2}}{v^{2}} + \frac{v^{2}}{\gamma^{2}}} \right\rbrack}$where H_(k) ^((p+1)) is the (p+1)th estimate and H_(k) ^((p)) the pthestimate of the channel coefficients, y_(k) is the received datacorresponding to the transmitted data x_(k), σ² is the channel noisevariance, {tilde over (H)}^((k)) is the channel coefficient vector Hwith a 0 on the kth component and Δ⁻¹, ν² and γ² have the meaningsindicated hereinabove.
 6. A method of channel estimation according toclaim 4, wherein the step of performing channel coefficient estimatescomprises replacing previously estimated channel coefficients of saidplurality of frequency domain sub-carriers with respective currentchannel coefficient estimates.
 7. A method of channel estimationaccording to claim 4, wherein repeating said step of performing channelcoefficient estimation comprises applying a cost function on anExpectation-Maximization algorithm on said plurality of frequency domainsub-carriers to improve said channel coefficient estimates.
 8. A methodof channel estimation according to claim 7, wherein said step ofperforming a channel coefficient estimation includes calculating anauxiliary function, the method further comprising the step of:performing a Maximisation process on said auxiliary function insubstantially the following manner:Q(H _(m) ,H _(m) ^((p)))=E _(x) _(m) [log P(x _(m) ,y _(m) ,{tilde over(H)} ^((m)) |H _(m))|y _(m) ,H _(m) ^((p)))].
 9. A method of channelestimation according to claim 4, wherein said step of performing achannel coefficient estimation comprises applying a forward-backwardalgorithm on said received signal to said plurality of channelcoefficient estimates in which estimates are made in a first order ofsaid plurality of frequency domain sub-carriers and subsequentlyestimates are made in a reversed order of said plurality of frequencydomain sub-carriers so as substantially to equalise an estimationaccuracy across said plurality of frequency domain sub-carriers.
 10. Asystem for channel estimation in an orthogonal frequency divisionmultiplexed (OFDM) receiver, the system comprising: demodulation meansfor applying Fourier transform to a received signal to obtain afrequency domain signal including a plurality of sub-carriers; decodingmeans for decoding the received signal and estimating probabilities ofcoded bits for at least said plurality of frequency domain sub-carriers;and channel estimation means for performing channel coefficientestimation for each of said plurality of frequency domain sub-carriersusing channel coefficient estimates for at least one other of saidplurality of frequency domain sub-carriers.